Bottom Line:
Genome-wide association (GWA) mapping of the temporal growth data resulted in the detection of time-specific quantitative trait loci (QTLs), whereas mapping of model parameters resulted in another set of QTLs related to the whole growth curve.The positive correlation between projected leaf area (PLA) at different time points during the course of the experiment suggested the existence of general growth factors with a function in multiple developmental stages or with prolonged downstream effects.In addition, the detection of QTLs without obvious candidate genes implies the annotation of novel functions for underlying genes.

Figure 6: Association profiles of SNPs that were identified by GWA mapping to be highly associated with the traits FW, PLA over time, and Expo2 model parameters. Each number in the columns with heading ‘FW’ or ‘PLA (mm2)’ represents the association found by univariate GWA mapping of growth trait by time point as indicated at the top of the column (FW at day 28 or PLA on one of the indicated days) and the SNP at the position indicated in the first two columns. In the last three columns, with the heading ‘Expo2’, the numbers indicate the association found between SNPs and parameters of model Expo2. Columns with the heading ‘Expo2: A0’ and ‘Expo2: r’ refer to univariate GWA mapping of model parameters ‘A0’ and ‘r’ respectively, and the column with heading ‘Expo2: full’ refers to multivariate GWA mapping of both growth model parameters. All SNPs with –log(P)>5 for at least one trait are shown. The intensity of the grey colour corresponds to the strength of the association. Curly brackets indicate that associated SNPs are located within 10kb and are considered as one QTL.

Mentions:
To identify the genetic basis of growth, GWA mapping was performed on PLA data (12 different dates), FW data (end point), and on the parameters derived from the selected growth model Expo2 (Fig. 5). Parameters of models with fits of r2<0.9 (11 out of 965) were excluded to avoid bias in detected associations due to outliers created by poor fits. PLA, FW, and model parameters were mapped as independent traits, even though they display covariance. The two parameters of Expo2, ‘r’ and ‘A0’, were also mapped simultaneously using an MTMM approach, which takes covariance of parameters into account (Korte et al., 2012). In total, 26 SNPs were highly associated [–log(P)>5] with one or more of the traits. One of these SNPs was associated with FW, 13 SNPs were associated with PLA, and 12 SNPs were associated with the model parameters. For each of these 26 strongly associated SNPs, an association profile was made to identify whether associations were specific for a trait or time point, or whether they were more general (Fig. 6). SNPs displaying a profile with strong associations for FW and PLA over time were not or only moderately associated with the Expo2 parameters (Fig. 6B). For example, the association profile of two SNPs at chromosome 5 at 8.8Mb was moderate to high for PLA at weeks 3 and 4 [–log(P) between 3.88 and 5.11], moderate for FW [–log(P)=3.85], and low for model parameters [–log(P)<2]. This trend was also observed conversely, although some SNPs that were highly associated with model parameters were also found to be moderately to highly associated with PLA at some time points (Fig. 6). For example, the association profile of the SNP at chromosome 3 at 1.2Mb that was high for parameter ‘A0’ [–log(P)=6.15] and for the multitrait analysis of ‘r’ and ‘A0’ [–log(P)=5.33] was also high for PLA in the third week [–log(P) between 4.01 and 4.97]. Remarkably, SNPs that were highly associated with model parameters were never associated with FW at day 28. This emphasizes that the model parameters reveal characteristics of growth that would not have been detected if only final plant size data were considered. Growth modelling, therefore, resulted in the detection of QTLs that would not have been found otherwise. Nonetheless, GWA mapping of model parameters cannot replace GWA mapping of plant size data, because both methods resulted in the detection of unique, highly associated, SNPs. SNPs that were selected because of strong association with PLA at a specific time point had an association profile for PLA that was relatively high [–log(P)>2] during the whole course of the experiment. This observation is in accordance with the significant positive correlation between PLA at different time points throughout the experiment (Fig. 2). These data indicate that the growth phenotype of a plant is the result of the interplay of many different genes and that the composition or contribution of this set of growth factors will change during the development of the plant. Some genes only play a role at a specific time point, whereas other more general regulators may have a function in growth for a longer period. Many transcription factor are, for example, known to be expressed in both a developmental time-specific and a tissue-specific manner (Turnbull, 2011), whereas their influence on plant development is visible during several developmental stages and, in other tissues, due to the expression of downstream targets. Similarly, levels of phytohormones are tightly regulated over time, whereas prolonged downstream effects are often observed (Schachtman and Goodger, 2008). The relative effect size of these regulators might change over time as a result of the dynamic balance between different regulatory components during development. The effect of these general growth factors will, therefore, only be large enough at specific time periods to be detectable with GWA mapping. SNPs that were selected because of strong association with PLA at a specific time point may, therefore, point to genes that play a role at a very specific period of development, but they may also point to more general regulators. If plant size had only been measured at one time point, many of these time-specific associations would not have passed the threshold, and thus would have been missed. Most striking is the observation that only one SNP was strongly associated [–log(P)>5] with FW at day 28, so if growth was only evaluated by end-point FW determination, all except one of the associations would have been missed. Thus, the analyses therefore show that evaluation of growth over time is more powerful to identify the underlying genetic factors than the evaluation of growth by end-point measurements. This is especially true when many small effect genes, whose relative contribution may change over time, are underlying the trait of interest.

Figure 6: Association profiles of SNPs that were identified by GWA mapping to be highly associated with the traits FW, PLA over time, and Expo2 model parameters. Each number in the columns with heading ‘FW’ or ‘PLA (mm2)’ represents the association found by univariate GWA mapping of growth trait by time point as indicated at the top of the column (FW at day 28 or PLA on one of the indicated days) and the SNP at the position indicated in the first two columns. In the last three columns, with the heading ‘Expo2’, the numbers indicate the association found between SNPs and parameters of model Expo2. Columns with the heading ‘Expo2: A0’ and ‘Expo2: r’ refer to univariate GWA mapping of model parameters ‘A0’ and ‘r’ respectively, and the column with heading ‘Expo2: full’ refers to multivariate GWA mapping of both growth model parameters. All SNPs with –log(P)>5 for at least one trait are shown. The intensity of the grey colour corresponds to the strength of the association. Curly brackets indicate that associated SNPs are located within 10kb and are considered as one QTL.

Mentions:
To identify the genetic basis of growth, GWA mapping was performed on PLA data (12 different dates), FW data (end point), and on the parameters derived from the selected growth model Expo2 (Fig. 5). Parameters of models with fits of r2<0.9 (11 out of 965) were excluded to avoid bias in detected associations due to outliers created by poor fits. PLA, FW, and model parameters were mapped as independent traits, even though they display covariance. The two parameters of Expo2, ‘r’ and ‘A0’, were also mapped simultaneously using an MTMM approach, which takes covariance of parameters into account (Korte et al., 2012). In total, 26 SNPs were highly associated [–log(P)>5] with one or more of the traits. One of these SNPs was associated with FW, 13 SNPs were associated with PLA, and 12 SNPs were associated with the model parameters. For each of these 26 strongly associated SNPs, an association profile was made to identify whether associations were specific for a trait or time point, or whether they were more general (Fig. 6). SNPs displaying a profile with strong associations for FW and PLA over time were not or only moderately associated with the Expo2 parameters (Fig. 6B). For example, the association profile of two SNPs at chromosome 5 at 8.8Mb was moderate to high for PLA at weeks 3 and 4 [–log(P) between 3.88 and 5.11], moderate for FW [–log(P)=3.85], and low for model parameters [–log(P)<2]. This trend was also observed conversely, although some SNPs that were highly associated with model parameters were also found to be moderately to highly associated with PLA at some time points (Fig. 6). For example, the association profile of the SNP at chromosome 3 at 1.2Mb that was high for parameter ‘A0’ [–log(P)=6.15] and for the multitrait analysis of ‘r’ and ‘A0’ [–log(P)=5.33] was also high for PLA in the third week [–log(P) between 4.01 and 4.97]. Remarkably, SNPs that were highly associated with model parameters were never associated with FW at day 28. This emphasizes that the model parameters reveal characteristics of growth that would not have been detected if only final plant size data were considered. Growth modelling, therefore, resulted in the detection of QTLs that would not have been found otherwise. Nonetheless, GWA mapping of model parameters cannot replace GWA mapping of plant size data, because both methods resulted in the detection of unique, highly associated, SNPs. SNPs that were selected because of strong association with PLA at a specific time point had an association profile for PLA that was relatively high [–log(P)>2] during the whole course of the experiment. This observation is in accordance with the significant positive correlation between PLA at different time points throughout the experiment (Fig. 2). These data indicate that the growth phenotype of a plant is the result of the interplay of many different genes and that the composition or contribution of this set of growth factors will change during the development of the plant. Some genes only play a role at a specific time point, whereas other more general regulators may have a function in growth for a longer period. Many transcription factor are, for example, known to be expressed in both a developmental time-specific and a tissue-specific manner (Turnbull, 2011), whereas their influence on plant development is visible during several developmental stages and, in other tissues, due to the expression of downstream targets. Similarly, levels of phytohormones are tightly regulated over time, whereas prolonged downstream effects are often observed (Schachtman and Goodger, 2008). The relative effect size of these regulators might change over time as a result of the dynamic balance between different regulatory components during development. The effect of these general growth factors will, therefore, only be large enough at specific time periods to be detectable with GWA mapping. SNPs that were selected because of strong association with PLA at a specific time point may, therefore, point to genes that play a role at a very specific period of development, but they may also point to more general regulators. If plant size had only been measured at one time point, many of these time-specific associations would not have passed the threshold, and thus would have been missed. Most striking is the observation that only one SNP was strongly associated [–log(P)>5] with FW at day 28, so if growth was only evaluated by end-point FW determination, all except one of the associations would have been missed. Thus, the analyses therefore show that evaluation of growth over time is more powerful to identify the underlying genetic factors than the evaluation of growth by end-point measurements. This is especially true when many small effect genes, whose relative contribution may change over time, are underlying the trait of interest.

Bottom Line:
Genome-wide association (GWA) mapping of the temporal growth data resulted in the detection of time-specific quantitative trait loci (QTLs), whereas mapping of model parameters resulted in another set of QTLs related to the whole growth curve.The positive correlation between projected leaf area (PLA) at different time points during the course of the experiment suggested the existence of general growth factors with a function in multiple developmental stages or with prolonged downstream effects.In addition, the detection of QTLs without obvious candidate genes implies the annotation of novel functions for underlying genes.